Dark-frame subtraction

About: Dark-frame subtraction is a research topic. Over the lifetime, 1216 publications have been published within this topic receiving 20763 citations.

Research on dark noise features of CMOS image sensor

Abstract: In this paper, the noises of images taken by Cannon40D and CMOS industrial camera are analyzed both in time and space domain without outside illumination. The dark random noise associated with time is very low, which means the system noise is almost uncorrelated with time. However, system noise in the center of the CMOS image sensor is lower than those in the marginal area, which suggests the noise presents a certain spatial distribution. Finally, taking the temperature effect into consideration, we find the relationship between the maximum noise and temperature.

Noise Image Coding Using Soft-Threshold Based Denoising

Abstract: The soft threshold method proposed by Donoho is studied in this paper. The noise standard deviation of noise image and the thresholds of different scale are given. A separable 2 D wavelet filter is used so that the soft threshold denoising by Donoho is expediently applied to image processing, such as simultaneous denosing makes compression rate of a noisy image be large farthest. For noise image of nature scene and SAR, different image compression schemes are proposed respectively. Especially for SAR image, a natural logarithm transfors multiplicative noise to additive noise so that SAR image can be suppressed via the soft threshold denoising scheme. Experimental results show the decoded images by the soft threshold denoising have better quality than by the hard threshold denoising. It indicates that the improved ways in this paper are effective for noise image compression.

Dark Frame Correction Via Bayesian Estimator in the Wavelet Domain

Abstract: It is generally known that every astronomical image which was acquired by CCD sensor, have to be corrected by dark frame. The Dark frame is mapping the dark current of the CCD. May become that we don't have the dark frame image and we cannot directly to correct the astronomical images. This work deals with dark frame correction based on Bayesian estimator in the wavelet domain. The models of the marginal probability density function (PDF) of the wavelet coefficients of astronomical images and dark frame images based on generalized Laplacian is used by this estimator. The parameters of the models, which were mentioned above, were estimated by least square error method on set of the images from our image database. The correction of the astronomical images by dark frame is better than the Bayesian estimator, but further work will deal with more sophisticated Bayesian estimator with more robust statistical description of the images.

A Perimeter-Based Noise Filter Algorithm for Vehicle Recognition

Abstract: Noise filter is the process of removing noise from a signal. In the area of image processing, the noise filter process is very important for the users. A new noise filter algorithm base on perimeter for vehicle recognition is proposed in this paper. Providing a edge detection methods based on Sobel operator, discussing the problem of noise in the boundary detected above, analysis the principle of the detailed solve approach for the problem, giving the implement of the algorithm. Experiments have been conducted by real vehicle images obtained from the real-time video produced by a monitor. The result shows that the new proposed method can remove the noise of the image signal for the vehicle recognition efficiently.

New effective method for removing impulse noise

Abstract: This paper proposes a new filtering method.First,we have got detail image from noise image and image of median filtering.Have used a noise detection method to get another image,which detains noises but removes details in detail image.Using this image,we can detect noises in corrupted image more accurately.To every pixel in noise image,the output of filter is the linear combination of original pixel value and the median of pixels inside windows of noise image.If the chance of corrupt pixel being noisy is big,then it will improve much in filtering process.Compared with other methods of median type,the proposed method can not only remove impulse more effectively,but also detain more details.